76 citations · 107 across the 8 of their papers we have counts for
9 papers · 1 filter
ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation
Haoxiang Guo, Shilin Liu, Hao Pan +3
We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders, i.e. vertices, edges and surface patch…
Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations
Peng-Shuai Wang, Yang Liu, Xin Tong
We present an adaptive deep representation of volumetric fields of 3D shapes and an efficient approach to learn this deep representation for high-quality 3D shape reconstruction an…
Semi-supervised 3D shape segmentation with multilevel consistency and part substitution
Chun-Yu Sun, Yu-Qi Yang, Hao-Xiang Guo +4
The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for…
Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks
Yu-Qi Yang, Peng-Shuai Wang, Yang Liu
Sparse voxel-based 3D convolutional neural networks (CNNs) are widely used for various 3D vision tasks. Sparse voxel-based 3D CNNs create sparse non-empty voxels from the 3D input…
Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields
Peng-Shuai Wang, Yang Liu, Yu-Qi Yang +1
Multilayer perceptrons (MLPs) have been successfully used to represent 3D shapes implicitly and compactly, by mapping 3D coordinates to the corresponding signed distance values or…
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction
Shi-Lin Liu, Hao-Xiang Guo, Hao Pan +3
Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry…